Channel Estimation for Millimeter-Wave Massive MIMO with Hybrid Precoding over Frequency-Selective Fading Channels
arXiv:1604.03049 · doi:10.1109/LCOMM.2016.2555299
Abstract
Channel estimation for millimeter-wave (mmWave) massive MIMO with hybrid precoding is challenging, since the number of radio frequency (RF) chains is usually much smaller than that of antennas. To date, several channel estimation schemes have been proposed for mmWave massive MIMO over narrow-band channels, while practical mmWave channels exhibit the frequency-selective fading (FSF). To this end, this letter proposes a multi-user uplink channel estimation scheme for mmWave massive MIMO over FSF channels. Specifically, by exploiting the angle-domain structured sparsity of mmWave FSF channels, a distributed compressive sensing (DCS)-based channel estimation scheme is proposed. Moreover, by using the grid matching pursuit strategy with adaptive measurement matrix, the proposed algorithm can solve the power leakage problem caused by the continuous angles of arrival or departure (AoA/AoD). Simulation results verify that the good performance of the proposed solution.
4 pages, 3 figures, Zhen Gao, Chen Hu, Linglong Dai and Zhaocheng Wang, "Channel estimation for millimeter-wave massive MIMO with hybrid precoding over frequency-selective fading channels," IEEE Communications Letters, vol. 20, no. 6, pp. 1259-1262, June 2016
References in corpus (1)
Cited by in corpus (26)
- Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems
- Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems
- Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces
- Channel Estimation and Hybrid Precoding for Frequency Selective Multiuser mmWave MIMO Systems
- Broadband Channel Estimation for Intelligent Reflecting Surface Aided mmWave Massive MIMO Systems
- Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications
- Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems
- Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems
- Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect
- Hybrid Beamforming Based on Implicit Channel State Information for Millimeter Wave Links
- Prospective Multiple Antenna Technologies for Beyond 5G
- Frequency-domain Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems
- Uplink Channel Estimation and Data Transmission in Millimeter-Wave CRAN with Lens Antenna Arrays
- Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field?
- Deep Convolutional Compression for Massive MIMO CSI Feedback
- Millimeter-Wave NOMA with User Grouping, Power Allocation and Hybrid Beamforming
- Channel Estimation for Hybrid Architecture Based Wideband Millimeter Wave Systems
- Angular-Domain Selective Channel Tracking and Doppler Compensation for High-Mobility mmWave Massive MIMO
- Wideband Hybrid Precoding for Next-Generation Backhaul/Fronthaul Based on mmWave FD-MIMO
- Wideband mmWave Channel Estimation for Hybrid Massive MIMO with Low-Precision ADCs
- A Frequency Domain Channel Estimation Based on Atomic Norm Minimization for Frequency Selective MmWave MIMO Systems
- Wideband Channel Estimation for THz Massive MIMO
- Broadband Synchronization and Compressive Channel Estimation for Hybrid mmWave MIMO Systems
- Nonconvex Regularized Gradient Projection Sparse Reconstruction for Massive MIMO Channel Estimation
- Low-Complexity Hybrid Beamforming for Massive MIMO Systems in Frequency-Selective Channels
- Compressed Sensing Channel Estimation for OFDM with non-Gaussian Multipath Gains